An identification of a person has required checking information, e.g., a password, and identification tools such as a key and a card. However, there is a drawback in that a user may forget the password or the identification tools may be lost or stolen. To that end, a great deal of research has been devoted to development of a biometric verification method for identifying a person based on his unique biometric characteristics.
Especially, a signature verification method is a representative example using behavioral biometric characteristics detected from a personal signature. The method is considered to have a lower accuracy in comparison with a method using other biometric characteristics, e.g., fingerprints. However, the method is culturally and socially acceptable and users may easily adapt themselves thereto, so that it is regarded as a useful method.
Since a signature is a pattern based on each person's name, every signature is different in its pattern, and therefore, one can be easily distinguished from another. However, in case similar patterns are intentionally generated (these patterns are called skilled forgery), the patterns are hardly distinguishable. Accordingly, it is required to distinguish the skilled forgery from a genuine signature.
When a signature is forged, a forger makes an effort to imitate an original pattern. Thus, it takes much longer to sign or a signing becomes slower at a certain point. These dynamic characteristics of a signing process are hardly forgeable and therefore are considered to be important information for distinguishing the skilled forgery from the original pattern.
Signature information captured by using a tablet digitizer includes information on a signature pattern and the dynamic characteristics of the signing process. However, information on the dynamic characteristics are not reflected to a completed signature pattern but available only in the form of additional information, so that the information thereon are hardly recognized by a pattern analysis method used for general signature recognition.
In order to solve such problem, the dynamic characteristics have been conventionally represented as simplified parameters such as a total signing time, an average velocity and an average acceleration.
However, a conventional method using the dynamic characteristics represented as the simplified parameters has disadvantages in that a considerable amount of information may be lost, which in turn causes deterioration of a distinction between signatures, i.e., genuine vs. forgery. As a result, a more efficient method for analyzing dynamic characteristics is required.